An Artificial Intelligence‐Based Motion Trajectory Prediction of Fibrous Matters

Abstract

Understanding and predicting the motion behavior of fibrous matters is critical for studying the structure, performance, function, and assembly behavior of fibrous matters. However, due to the limitation of characterization technology on a space−time scale, the existing technologies cannot provide real‐time in situ tracking of entire dynamic processes of fibrous matters. To address this shortcoming, an artificial intelligence‐based codebase, named the artificial intelligence‐based fiber motion tracking (AI‐FMT), is proposed for analyzing, studying, and predicting motion behaviors of fibrous matters. The proposed AI‐FMT can automatically extract morphology information of fibrous matters from a large number of pictures or video streams, which serve as an input for machine learning to predict the motion pattern of fibrous matters. Using a finite‐element concept in understanding the point−coordinate relationship, the AI‐FMT greatly simplifies the architecture of the neural network during motion prediction. For the case of 928 trainable parameters, an average accuracy of 97.8% is achieved in predicting variations in morphological parameters, such as mean square radius of gyration, end‐to‐end distance, and curvature in the movement process of animal hair. The presented results can help to understand the structure, assembly, performance, and function of fibrous matters.

Keywords

• fibrous matters
• machine learning
• motion behaviors
• structure−property−function relationships